Found 9746 publications. Showing page 19 of 390:
2011
2015
2003
2017
2014
This report presents the ICP Materials database for the period October 2011-December 2012. It includes environmental data from the ICP Materials trend exposure programme for 2011 - 2012. The database consists of meteorological data (T and RH), and pollution data as gas concentrations, amounts of ions in precipitation, particle concentration and amount of particle deposition.
2014
The paint steel panels coated with two layers alkyd paint (total 80 µm) have been exposed for 1 and 2 years. The damage of the paint system has been evaluated by using the well-established ASTM-standards. Damage from the cut is observed at all test sites, and after two years. ASTM values from 9 to 6 are observed. Comparing the results with the environmental parameters seems to indicate that the damage is affected by air pollution like in the big cities. However, the damage is also surprisingly high at rural sites, particularly at sites with high amount of rain.Filiform corrosion, shown as thin whiskers under the paint film from the cut, is more dominated in the second phase of the programme than in first phase 1987-1989.Cracking of the corrosion protection paint films has been observed on samples from the Tel Aviv site after two years, but was never observed in the first phase of the programme.
2000
This report presents the ICP Materials database for the period October 2014 - October 2015. It includes environmental data from the ICP Materials trend exposure programme for 2014 - 2015. The database consists of meteorological data (T and RH) and pollution data as gas concentrations, amounts of ions in precipitation, particle concentrations and amounts of particle deposition.
2017
2001
2001
Ultraviolet exposure scenarios: risks of erythema from recommendations on cutaneous vitamin D synthesis. Advances in Experimental Medicine and Biology, 624
2008
2012
2009
2014
2007
Two-Stage Feature Engineering to Predict Air Pollutants in Urban Areas
Air pollution is a global challenge to human health and the ecological environment. Identifying the relationship among pollutants, their fundamental sources and detrimental effects on health and mental well-being is critical in order to implement appropriate countermeasures. The way forward to address this issue and assess air quality is through accurate air pollution prediction. Such prediction can subsequently assist governing bodies in making prompt, evidence-based decisions and prevent further harm to our urban environment, public health, and climate, all of which co-benefit our economy. In this study, the main objective is to explore the strength of features and proposed a two stage feature engineering approach, which fuses the advantage of influential factors along with the decomposition approach and generates an optimum feature combination for five major pollutants including Nitrogen Dioxide (NO 2 ), Ozone (O 3 ), Sulphur Dioxide (SO 2 ), and Particulate Matter (PM2.5, and PM10). The experiments are conducted using a dataset from 2015 to 2020 which is publicly available and is collected from Belfast-based air quality monitoring stations in Northern Ireland, UK. In stage-1, using the dataset new features such as trigonometric and statistical features are created to capture their dependency on the target pollutant and generated correlation-inspired best feature combinations to improve forecasting model performance. This is further enhanced in stage-2 by an optimum feature combination which is an integration of stage-1 and Variational Mode Decomposition (VMD) based features. This study employed a simplified Long Short Term Memory (LSTM) neural network and proposed a single-step forecasting model to predict multivariate time series data. Three performance indicators are used to evaluate the effectiveness of forecasting model: (a) root mean square error (RMSE), (b) mean absolute error (MAE), and (c) R-squared (R 2 ). The results demonstrate the effectiveness of proposed approach with 13% improvement in performance (in terms of R 2 ) and the lowest error scores for both RMSE and MAE.
IEEE (Institute of Electrical and Electronics Engineers)
2024
2004
2016
2017